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Open Access Highly Accessed Research article

Evaluation of gene-expression clustering via mutual information distance measure

Ido Priness, Oded Maimon and Irad Ben-Gal*

Author Affiliations

Department of Industrial Engineering, Tel Aviv University, Israel

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BMC Bioinformatics 2007, 8:111  doi:10.1186/1471-2105-8-111

Published: 30 March 2007

Additional files

Additional file 1:

Used software and parameters for comparison of clustering algorithms. The file contains the following 2 sections. Section 1: A comparison study of the Mutual Information (MI) measure, the Euclidean distance and the Pearson correlation coefficient. The robustness comparison is performed by using four public gene expression datasets. Each dataset contains two types of samples with a clear biological distinction, leading to a 'true' bi-clustering solution. Section 2: Details of the underlying concepts and parameters of the four clustering algorithms that were used in experiment 2. Additionally, the full results of the algorithms comparison are presented.

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